Visium
Mouse brain
GSM8638006
bronze H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8638006_064-D_hires_image': DataArray[cyx] (3, 1162, 2000)
│ └── 'GSM8638006_064-D_lowres_image': DataArray[cyx] (3, 349, 600)
├── Shapes
│ └── 'GSM8638006_064-D': GeoDataFrame shape: (3473, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3473, 32286)
with coordinate systems:
▸ 'GSM8638006_064-D', with elements:
GSM8638006_064-D_hires_image (Images), GSM8638006_064-D_lowres_image (Images), GSM8638006_064-D (Shapes)
▸ 'GSM8638006_064-D_downscaled_hires', with elements:
GSM8638006_064-D_hires_image (Images), GSM8638006_064-D (Shapes)
▸ 'GSM8638006_064-D_downscaled_lowres', with elements:
GSM8638006_064-D_lowres_image (Images), GSM8638006_064-D (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("1yb4fCV9kSozpZBq0001")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) Python · lamindb
Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("1yb4fCV9kSozpZBq") # stem uid: Lamin resolves it to the latest version
sdata = artifact.load() # cache, then load into memory
group = artifact.open() # stream without downloading
path = artifact.cache() # sync into the local cache, returns the path Shell
# sddb client: download the .zarr into data/
sddb-cli download 1yb4fCV9kSozpZBq0001 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/1yb4fCV9kSozpZBq
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 1yb4fCV9kSozpZBqLicense: NCBI GEO policy
Part of collection
Spatial transcriptomics identifies regional disruptions in brain rhythmic gene expression in a mouse model of Alzheimer’s disease→ 34 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- brain
- Modality
- RNA
- Observations (spots)
- 3,473
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,162 px
- mpp
- 5.832 µm/px
- Size
- 46.9 MB
- Sample ID
- GSM8638006
- License
- NCBI GEO policy
- Published
- Mar 13, 2026
Cite this dataset
doi.org/10.64898/2026.01.26.701799 ↗ BibTeX
@article{Gelber_2026,
title = {Spatial transcriptomics reveals brain-wide circadian disruption in an Alzheimer’s disease model},
url = {http://dx.doi.org/10.64898/2026.01.26.701799},
DOI = {10.64898/2026.01.26.701799},
publisher = {openRxiv},
author = {Gelber, Alon and Romero, Haylie and Burrows, Dominic and Whittaker, Daniel S. and Carlin, Daniel and Mukamel, Eran A. and Desplats, Paula},
year = {2026},
month = Jan
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB